A Multi-Agent System Approach for Algorithm Parameter Tuning.
ABSTRACT The parameter setting of an algorithm that will result in optimal performance is a tedious task for users who spend a lot
of time fine-tuning algorithms for their specific problem domains. This paper presents a multi-agent tuning system as a framework to set the parameters of a given algorithm which solves a specific problem. Besides, such a configuration
is generated taking into account the current problem instance to be solved. We empirically evaluate our multi-agent tuning system using the configuration of a genetic algorithm applied to the root identification problem. The experimental results show
the validity of the proposed model.
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Chapter: Introduction to Bayesian Networks[Show abstract] [Hide abstract]
ABSTRACT: Reasoning with incomplete and unreliable information is a central characteristic of decision making, for example in industry, medicine and finance. Bayesian networks provide a theoretical framework for dealing with this uncertainty using an underlying graphical structure and the probability calculus. Bayesian networks have been successfully implemented in areas as diverse as medical diagnosis and finance. We present a brief introduction to Bayesian networks for those readers new to them and give some pointers to the literature.Bayesian Networks: A Practical Guide to Applications, 03/2008: pages 1 - 13; , ISBN: 9780470994559
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ABSTRACT: David Goldberg's Genetic Algorithms in Search, Optimization and Machine Learning is by far the bestselling introduction to genetic algorithms. Goldberg is one of the preeminent researchers in the field--he has published over 100 research articles on genetic algorithms and is a student of John Holland, the father of genetic algorithms--and his deep understanding of the material shines through. The book contains a complete listing of a simple genetic algorithm in Pascal, which C programmers can easily understand. The book covers all of the important topics in the field, including crossover, mutation, classifier systems, and fitness scaling, giving a novice with a computer science background enough information to implement a genetic algorithm and describe genetic algorithms to a friend.
01/1995; Wiley., ISBN: 978-0-471-57148-3